• Intro & general tips
• R performance tips: patterns to use and avoid
• Vectors & Matrices
• Memory management, large tables
• Profiling and Benchmarking
• Loops

• If you don't understand something, try some experiments
• Browse the documentation, learn its jargon and quirks
• Break your code into functions when appropriate
• Use functions to reduce the need for global variables
• Write tests for your functions
• Use git to keep track of changes
• Learn to distribute your code as a package

• Designed to make programming easier
  • Speed was not the primary design criteria
• Slow programs often a result of bad programming practices or not understanding how R works
• There are various options for calling C or C++ functions from R

• Become familiar with R's vector and apply functions
• Consider specialized performance packages
• E.g. data.table, bigmemory, plyr, RSQLite, snow, multicore, parallel
• Don't use an R GUI when performance is important

• Be methodical but don't get carried away with micro-optimizations
• Use monitoring tools such as top, Activity Monitor, Task Manage
• Use vector functions
• Avoid duplication of objects
• Pre-allocate result vectors
• Profile your code and run benchmarks
• Byte-compile with cmpfun, or call a compiled language (e.g. C, C++)

• Fast, since implemented as a single C or Fortran function
• Concise and easy to read
• Can often replace for loops
• However, heavy use can result in high memory usage

• math operators: +, -, *, /, ^, %/%, %%
• math functions: abs, sqrt, exp, log, log10, cos, sin, tan, sum, prod
• logical operators: &, |, !
• relational operators: ==, !=, <, >, <=, >=
• string functions: nchar, tolower, toupper, grep, sub, gsub, strsplit
• conditional function: ifelse (pure R code)
• misc: which, which.min, which.max, pmax, pmin, is.na, any, all, rnorm, runif, sprintf, rev, paste, as.integer, as.character

initialize x and fill it with zeros

n <- 10
x <- double(n)
x

 [1] 0 0 0 0 0 0 0 0 0 0

Extend x by assignment

x[15] <- 100
x

 [1]   0   0   0   0   0   0   0   0   0   0  NA  NA  NA  NA 100

Resize/truncate x

length(x) <- 5
x

[1] 0 0 0 0 0

rnorm vector function

x <- rnorm(10)
x

 [1] -0.15836239 -1.43148503 -0.98957681 -1.05556511 -0.05897849
 [6]  0.32558374  0.39072691  0.17596137  0.38024952 -1.08059739

Extract subvector

x[3:6]

[1] -0.98957681 -1.05556511 -0.05897849  0.32558374

Extract elements using result of vector relational operation

x[x > 0]

[1] 0.3255837 0.3907269 0.1759614 0.3802495

You can also use an index to assign values

x[is.na(x)] <- 0

Make a new matrix

m <- matrix(rnorm(100), 10, 10)

Extract 2X3 submatrix (non-consecutive columns)

m[3:4, c(5,7,9)]

          [,1]      [,2]      [,3]
[1,] -1.194728  0.184040  1.125859
[2,]  0.558309 -0.212854 -1.513369

Extract arbitrary elements as vector

m[cbind(3:6, c(2,4,6,9))]

[1] -0.00151433  0.42465008 -0.24771947  0.87036074

Extract elements using result of vector relational operation

head(m[m > 0])

[1] 0.8362644 0.7029953 0.5499310 0.4274378 0.6290166 0.2998121

You can also use a matrix index to assign values

m[is.na(m)] <- 0

• Avoid duplicating objects, especially big ones or those in loops
• Look into memory effecient libraries
• Look into other formats to store data

• R uses pass by value semantics for function arguments
• In general, this requires making copies of objects
  • Functions must return modified object
• R tries to avoid copying unless necessary

tracemem reports when an object is duplicated, which is very useful for debugging performance problems.

In this example, object duplication is expected and helpful.

x <- double(10)
tracemem(x)

[1] "<0x5642bee7d5f8>"

y <- x
y[1] <- 10

tracemem[0x5642bee7d5f8 -> 0x5642bec45c08]: eval eval withVisible withCallingHandlers handle timing_fn evaluate_call <Anonymous> evaluate in_dir block_exec call_block process_group.block process_group withCallingHandlers process_file <Anonymous> <Anonymous> 

.Internal(inspect(x))

@5642bee7d5f8 14 REALSXP g0c5 [NAM(2),TR] (len=10, tl=0) 0,0,0,0,0,...

.Internal(inspect(y))

@5642bec45c08 14 REALSXP g0c5 [NAM(1),TR] (len=10, tl=0) 10,0,0,0,0,...

x[1] <- 50

tracemem[0x5642bee7d5f8 -> 0x5642bebfb3d8]: eval eval withVisible withCallingHandlers handle timing_fn evaluate_call <Anonymous> evaluate in_dir block_exec call_block process_group.block process_group withCallingHandlers process_file <Anonymous> <Anonymous> 

> m <- matrix(0, 3, 3)
> tracemem(m)
[1] "<0x7fc168d29df0>"
> m[1,1] <- 1
> nrow(m)
[1] 3
> m[1,1] <- 2
tracemem[0x7fc168d29df0 -> 0x7fc168d21f58]:

R makes it easy to read entire data sets in one operation, but reading it in parts can be much more efficient.

• Splitting the problem into smaller tasks is compatible with parallel computing techniques
• The foreach & iterators packages provide tools to split inputs into smaller pieces
• Use Linux commands (split, awk, etc) or other languages (e.g. Python to preprocess
  • Split data files and remove unneeded fields

The read.table function is commonly used for reading data files, but it can be very slow on large files

• Use of the colClasses argument can improve performance
• colClasses can be used to skip a column, using less memory
• It can be faster to read a file in smaller chunks using the nrows argument
• The scan function can be faster
• Consider using similar functions from different packages, such as data.table, sqldf, and bigmemory

bigmemory

• Defines mutable matrix objects that aren't automatically duplicated
• works very well in conjunction with parallel computing

big.matrix

• can use a backing file that is memory mapped

biganalytics

• apply, biglm, bigglm, bigkmeans, colmax

bigtabulate

• bigsplit, bigtabulate, bigtable, bigtsummary

Saving data in a binary format can make it much faster to read the data later. There are a variety of functions available to do that:

• save/load
• writeBin/readBin
• write.big.matrix/read.big.matrix (from bigmemory)

Consider putting data into an SQLite database.

• RSQLite package is easy to use
• Easy to get subsets of the data into a data frame
• Command line tool very useful for experimenting with queries
• Database can be accessed from many different languages
• The sqldf package may be useful also
• Can be quite slow

Profiling

• If you've decided you need your code to perform better, profile first
• Profiling helps isolate hot spots
• Time spent here will likely yeild best ROI

Benchmarking

• With hot spots in hand, examine the code and propose alternatives
• While ensuring the reesults are the same, ask which performs best

R has builtin support for profiling, but there are additional

packages available:

• proftools
• profvis (RStudio integration)

f <- function(a) { g1(a) + g2(2 * a) }

g1 <- function(a) { h(a) }

g2 <- function(a) { sqrt(a) }

h <- function(a) {
  b <- double(length(a))
  for (i in seq_along(a)) {
    b[i] <- sqrt(a[i])
  }
  b
}

x <- 1:1000000
Rprof('prof.out')
for (i in 1:10) {
  y <- f(x)
}
Rprof(NULL)
summaryRprof("prof.out")$by.self

         self.time self.pct total.time total.pct
"h"           0.76    55.07       0.82     59.42
"g2"          0.44    31.88       0.54     39.13
"*"           0.06     4.35       0.06      4.35
"double"      0.06     4.35       0.06      4.35
"sqrt"        0.04     2.90       0.04      2.90
"+"           0.02     1.45       0.02      1.45

Can also do this in RStudio, e.g. Profile -> Start Profile

profvis({
for (i in 1:10) {
  y <- f(x)
}
})

Knowing where code is slow via profiling, use benchmarking tools

• Put problem code into a functions
• Benchmark different versions of code for comparison
• system.time is useful for long running code
• microbenchmark package is useful for analyzing short running code

• Not all for loops are bad
• Most common mistakes involve for loops.
• The classic mistake is not preallocating a result vector.

Create a vector of length n where all values are x

bad.for <- function(n,x) {
  result <- NULL
  for (i in 1:n) {
    result[i] <- x
  }
  result
}

• Large number of iterations
• Tiny amount of computation per iteration
• Item result vector is reallocated and copied on each iteration
• Triggering garbage collection periodically

okay.for <- function(n,x) {
  result <- double(n)
  for (i in 1:n) {
    result[i] <- x
  }
  result
}

Improvement over the previous example, but it's still slow because of the many tiny iterations.

strange.for <- function(n, x) {
  result <- NULL
  for (i in n:1) {
    result[i] <- x
  }
  result
}

Is this loop faster or slower than the previous two?

# use of vector assignment
vector.assn <- function(n, x) {
  result <- double(n)
  result[] <- x
  result
}

We can also use vector assignment

built.in <- function(n, x) {
  rep(x, n)
}

Or, we could read the fine manual and use a built-in function

n <- 10000
x <- 7
bad.result        <- bad.for(n, x)
okay.result       <- okay.for(n, x)
strange.result    <- strange.for(n, x)
vector.result     <- vector.assn(n, x)
built.result      <- built.in(n, x)
c(identical(bad.result, okay.result),
identical(bad.result, strange.result),
identical(bad.result, vector.result),
identical(bad.result, built.result))

[1] TRUE TRUE TRUE TRUE

res <- microbenchmark(bad=bad.for(n,x), okay=okay.for(n,x), strange=strange.for(n,x),
                      vector=vector.assn(n,x), builtin=built.in(n,x))
kable(summary(res, unit="relative"))

autoplot(res)

Create a matrix with n rows and x columns

Each value in the matrix is sampled from normal distribution, \(\mu=0 , \sigma=1\)

Generate a matrix of values sampled from normal distribution n rows, x columns

bad.norm <- function(n,x) {
  m <- NULL
  for (i in 1:n) {
    m <- rbind(m, rnorm(x))
  }
  m
}

Just like before, we build a matrix and populate it with a for loop

ok.norm <- function(n,x) {
  m <- matrix(0, nrow=n, ncol=x)
  for (i in 1:n) {
    m[i,] <- rnorm(100)
  }
  m
}

don't need to preallocate matrix

lapply.norm <- function(n,x) {
  do.call('rbind', lapply(1:n, function(i) rnorm(x)))
}

best.norm <- function(n,x) {
  m <- rnorm(x * n)
  dim(m) <- c(x, n)
  t(m)
}

n <- 600
x <- 100
# Verify correct results
set.seed(123); bad.result <- bad.norm(n,x)
set.seed(123); ok.result <- ok.norm(n,x)
set.seed(123); lapply.result <- lapply.norm(n,x)
set.seed(123); best.result <- best.norm(n,x)

c(identical(bad.result, ok.result),
identical(bad.result, lapply.result),
identical(bad.result, best.result))

[1] TRUE TRUE TRUE

res <- microbenchmark(bad=bad.norm(n,x), ok=ok.norm(n,x),
                        lapply=lapply.norm(n,x), best=best.norm(n,x))
kable(summary(res, unit="relative"))

autoplot(res)

Results in your function as bytecode

fun.for <- function(x, seed=1423) {
  set.seed(seed)
  y <- double(length(x))
  for (i in seq_along(x)) {
    y[i] <- rnorm(1) * x[i]
  }
  y
}
fun.for.compiled <- cmpfun(fun.for)

x <- 10000
res <- microbenchmark(fun.for=fun.for(x),
                      fun.for.compiled=fun.for.compiled(x))
kable(summary(res, unit="relative"))

autoplot(res)

The standard parallel package includes a very useful function: mclapply

• mclapply is nearly a drop-in replacement for lapply
• mc.cores argument specifies number of workers to use
• does not execute in parallel on Windows (depends on fork system call)
• not generally safe to use in R GUIs (seems to work in RStudio)

library(parallel)
library(randomForest)

x <- matrix(runif(500), 100)
y <- gl(2, 50)
ntree <- 1000
cores <- detectCores()
vntree <- rep(ntree %/% cores, cores)
worker <- function(n) randomForest(x, y, ntree=n)
rf <- do.call('combine',
              mclapply(vntree, worker, mc.cores=cores))

• This presentation: https://github.com/ycrc/efficient-R
• R Book: http://adv-r.had.co.nz
• R Manuals: http://cran.r-project.org/manuals.html
• R Inferno: http://www.burns-stat.com/documents/books/the-r-inferno